Peer-Grading at Scale with Rank Aggregation

Lin Ling, Chee-Wei Tan
{"title":"Peer-Grading at Scale with Rank Aggregation","authors":"Lin Ling, Chee-Wei Tan","doi":"10.1145/3430895.3460980","DOIUrl":null,"url":null,"abstract":"Thanks to the wide availability of the internet and personal computing devices, online teaching methods like Massive Open Online Courses (MOOC) are becoming an essential part of modern education. While these methods enable educators to reach much more students, the massive volume of assignments to grade places a heavy burden on the instructors. Most online courses remedy this by restricting the question types to simple forms or performing naive peer-grading. These approaches are either too restricted to capture students' learning level, or require heavy supervision from the instructors to ensure the grades are fair. In this paper, we propose a rank-aggregation-based peer-grading method that estimates the quality of each assignment and the probability that each student is grading unbiasedly. The estimation errors have theoretical upper-bounds, and the bounds can be proved to tighten when the problem size increases, which is confirmed by our numerical experiment.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Eighth ACM Conference on Learning @ Scale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3430895.3460980","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Thanks to the wide availability of the internet and personal computing devices, online teaching methods like Massive Open Online Courses (MOOC) are becoming an essential part of modern education. While these methods enable educators to reach much more students, the massive volume of assignments to grade places a heavy burden on the instructors. Most online courses remedy this by restricting the question types to simple forms or performing naive peer-grading. These approaches are either too restricted to capture students' learning level, or require heavy supervision from the instructors to ensure the grades are fair. In this paper, we propose a rank-aggregation-based peer-grading method that estimates the quality of each assignment and the probability that each student is grading unbiasedly. The estimation errors have theoretical upper-bounds, and the bounds can be proved to tighten when the problem size increases, which is confirmed by our numerical experiment.
在等级聚合的规模上的同侪分级
由于互联网和个人计算设备的广泛使用,像大规模开放在线课程(MOOC)这样的在线教学方法正在成为现代教育的重要组成部分。虽然这些方法使教育者能够接触到更多的学生,但大量的作业给教师带来了沉重的负担。大多数在线课程通过将问题类型限制为简单的形式或执行幼稚的同行评分来解决这个问题。这些方法要么过于局限,无法捕捉学生的学习水平,要么需要教师的大力监督,以确保成绩公平。在本文中,我们提出了一种基于排名聚合的同伴评分方法,该方法可以估计每个作业的质量和每个学生无偏评分的概率。该估计误差具有理论上界,且随着问题规模的增大,估计误差的上界会变紧,数值实验也证实了这一点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信